More recently, learning-based approaches have been introduced that enable dense depth estimation by exploiting priors learned from training images. In particular, monocular depth estimation that leverages only a single image together with learned priors has become a popular area of research, where deep neural networks are used to implement models that directly predict a depth map for given input image [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. While the basic idea is simple and attractive, the accuracy of the monocular depth estimation methods is limited by the lack of strong geometric constraints such as parallax. Thus, considerably more accurate depth maps can be achieved with deep learning based multi-view stereo methods [7]}, [8]}, [9]}, [10]}. However, the accuracy comes at the cost of increased computational complexity as multiple images need to be aggregated by the network to produce a single depth map.
